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Time to Accept Risk in Defense Acquisitions | Center for Security and Emerging Technology
Analysis·4 min read

Who Pays When AI Firms Falter? The Quiet Public Bailout Built Into Policy

By Jordan Vale

A policy loop is forming: taxpayers underwrite expensive model training, regulators relax rules to speed deployment, and government contracts backstop risky startups. The result is an implicit social insurance for an industry that, if markets correct, could leave public coffers and civic systems holding the bill.

On Nov. 13, 2025, the AI Now Institute argued bluntly that "the federal government is already bailing out the AI industry with regulatory changes and public funds that will protect companies in the event of a private sector pullback." That line lands because it names a structural problem: governments are subsidizing compute, data, and market access even as firms chase scale and novel risks.

How taxpayers already underwrite AI’s riskiest bets

This matters now because the economics of large models plus recent policy moves create asymmetric downside. Training a state-of-the-art foundation model can require weeks on thousands of GPUs and tens of millions of dollars in cloud spending. At the same time, policymakers - from defense procurement offices to regulatory agencies - are loosening barriers and promising sizable contracts. The combination concentrates profit in a few winners while scattering social costs across citizens, public budgets, and democratic institutions.

The subsidy mechanism is straightforward and multilayered. Public universities supply talent; government-funded research seeds algorithms; public datasets and open-source software reduce development costs; and taxpayer-funded cloud credits and contracts absorb commercialization risk. AI Now’s Nov. 13, 2025, commentary frames these elements as a de facto backstop: regulatory and fiscal choices make it likelier that firms survive private downturns.

Defense procurement’s push and the politics of accepting risk

Concrete examples add up. Government research grants from agencies such as the National Science Foundation and the Defense Advanced Research Projects Agency routinely fund early-stage AI work. Universities graduate tens of thousands of data scientists and engineers annually, lowering hiring costs for startups. Meanwhile, public procurement can de-risk revenue: a single multiyear contract from a federal agency can represent more than 25% of annual income for a midsize AI vendor, according to procurement analysts.

The cost side is stark. Training GPT-scale models uses thousands of GPU-days; published estimates place large-model training bills in the tens to low hundreds of millions of dollars for the biggest builds. That expense creates strong incentives for governments to prop capacity rather than let firms fail and lose sunk public investment in talent, data, and infrastructure.

Societal risks that get offloaded when firms stay standing

The Center for Security and Emerging Technology argued on Nov. 10, 2025, that defense acquisitions must accept more risk to field new capabilities quickly. That calculus matters because defense contracts are not simple purchases; they come with stockpiles of data access, testbeds, and indemnities that shelter vendors from typical market discipline.

When a defense buyer tolerates immature systems, it also internalizes failure costs: delayed deployments, safety incidents during testing, and the expense of maintaining or unwinding unviable programs. CSET authors warn that faster fielding can help national security, but only if procurement offices pair speed with accountability mechanisms-metrics-based milestones, escrowed source code, and financial penalties for unmet thresholds.

Absent those guardrails, the political logic favors keeping projects alive. Agencies do not want to explain sunk costs; Congress tends to fund iterative fixes rather than program cancellations. The result is a practical subsidy: taxpayer dollars extend the runway of risky AI firms while private investors see downside limited.

Policy levers that can align risk and responsibility

Societal risks that get offloaded when firms stay standing

Partnership on AI’s Oct. 31, 2025, 'Horror Index' catalogs harms that scale with wider deployment: election interference, automated surveillance, digital sweatshops that underpin data pipelines, and model collapse, where models increasingly train on AI-generated outputs. Each of these imposes social costs-misinformation, privacy erosions, labor exploitation, degraded knowledge ecosystems-that are difficult to price into private contracts.

For example, automated surveillance systems procured for public-safety use can disproportionately harm marginalized communities. When jurisdictions purchase AI tools without strict oversight, deployment decisions shift harm from vendors to citizens. The marketplace failure is therefore not just financial; it is civic, diffusing harms across institutions not equipped to bear them.

These externalities magnify the bailout problem. If public funds keep vendors solvent, the public also assumes long-term remediation burdens: audits, litigation, retraining programs for displaced workers, and environmental impacts from increased data center use.

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